Systems and methods for providing adaptive tools for enabling collaborative and integrated decision-making

ABSTRACT

A system and method for presenting data relating to at least one individualized instructional program, comprising: receiving filtering criteria, accessing at least one repository of data relating to the individualized instructional program, and identifying data responsive to the filtering criteria.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a Divisional of U.S. application Ser. No.10/657,562, filed Sep. 9, 2003. U.S. application Ser. No. 10/657,562claims priority from U.S. Provisional Application Ser. No. 60/408,875filed Sep. 9, 2002, and U.S. Provisional Application Ser. No.60/432,661, filed Dec. 12, 2002. The entirety of all above-listedApplications are incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to systems and methods forpresenting data, and specifically to systems and methods for presentingfiltered data.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates a dynamic intersection of career, user attributes,and educational programs, according to one embodiment of the presentinvention.

FIGS. 2A-2H illustrate logic diagrams indicating how levels andsublevels are filtered, according to one embodiment of the presentinvention.

FIG. 2I is a flowchart diagram illustrating an example of how levels andsublevels are filtered, according to one embodiment of the presentinvention.

FIGS. 3A and 3B illustrate application overview 300, according to oneembodiment of the present invention.

FIG. 4 illustrates a method of filtering data 400, according to oneembodiment of the present invention.

FIG. 5 illustrates a method of using an adaptive graphical userinterface 500, according to one embodiment of the present invention.

FIG. 6 illustrates a method of using an assessment combinator 600,according to one embodiment of the present invention.

FIG. 7 illustrates a method of using a self-concept assessor 700,according to one embodiment of the present invention.

FIG. 8 illustrates a method of using an education plan designer 800,according to one embodiment of the present invention.

FIG. 9 illustrates a method of using an adaptive backsteppable filter900, according to one embodiment of the present invention.

FIG. 10 illustrates a method of using an assessment manager 1000,according to one embodiment of the present invention.

FIGS. 11A-11S illustrate curricular design screen shots.

FIGS. 12A-12U illustrate explore/job market screen shots.

Additional features and advantages of the present invention will becomemore apparent from the detailed description set forth below when takenin conjunction with the Figures in which like reference numbers indicateidentical or functionally similar elements.

DESCRIPTION OF THE INVENTION

While pursuing education and career objectives and goals, prospectiveand continuing students, face many questions. Correct and timely answersto these questions are very important, particularly early in the life ofa student, as they could make the emotional and financial differencebetween staying on-track towards personal goals, or being off-trackwithout knowing it.

The present invention comprises a system and method for presenting datarelating to at least one individualized instructional program,comprising: receiving filtering criteria, accessing at least onerepository of data relating to the individualized instructional program,and identifying data responsive to the filtering criteria.

The present invention develops and presents an optimalindividual-matched and integrated education and career plan. The presentinvention provides decision support techniques for obtaining integratededucation-career planning and implementation solutions, supported by avast education and career knowledge base. In one embodiment, the presentinvention is used for middle school through college education levels. Inother embodiments, the present invention is used for other levels ofeducation, including pre-school, elementary, and post graduateeducation.

The present invention allows students to acquire quick, accurate,complete, and comprehensive answers to questions related to careerpossibilities and potential educational paths to these careers.

The present invention also allows guidance counselors and advisors toquickly and efficiently create a rigorous education and career planoptimized for each individual student. The present invention integratespotential careers, potential education programs, and student attributesin an easy-to-use package that each student can use to do most of thework, optionally with some assistance or review by a parent orcounselor.

The present invention can be used in any educational field, including,for example: engineering, computers and information technology, health,physical, biological and life sciences, business management, education,and social and behavioral science.

While the present invention is described in the context of education,those experienced in the art will see that use outside the educationfield is possible. Potential other fields include, for example: use bylocal governments and states to rapidly analyze the general careerdirection of students for policy making projection of state workforcelevels; use in preventive medicine and health management, to produce atreatment explorer to explore and investigate optimal individualizedoptions (e.g., on the basis of personal traits and family history) indiagnosis and treatment for diseases, even before the onset of disease;and use in designing individualized financial portfolios for exploringand investigating optimal individualized options in financial products.In these cases, the education-related databases described below arereplaced by other databases relevant to the field (e.g., a diseasediagnostic and treatment database).

Intersection of Career, User Attributes, and Educational Programs

FIG. 1 illustrates a dynamic intersection of career, user attributes,and educational programs, according to one embodiment of the presentinvention.

FIG. 2A illustrates four levels of support functions: primary (e.g.,Institutions, Funding & Employers) 205, secondary (e.g., Pursuits) 210,tertiary (e.g., Programs & Standards) 215, and quaternary (e.g.,Curricula & Courses) 220, according to one embodiment of the presentinvention. A user may use the present invention at any level, dependingon the need to seek either general decision support, or increasinglyspecific decision support. Users also have flexibility to limit searchesto an education universe only, a career universe only, or an integratededucation-career universe, with or without personal attributeintegration. All levels are structurally interlinked for full,integrated functionality.

The primary level 205 provides integrated education-career explorationand investigation. This exploration and investigation is matched to auser, in an alternate embodiment. For example, a user can enter arequest for information on colleges that provide special academicprograms for certified musicians to be trained as computer programmers,preferably located in the rural U.S., close to branches of major ITcompanies, with an admission policy that accommodates someone with ahigh school GPA of 2.5, and an SAT score of 1000. At the secondary 210and tertiary 215 levels, the present invention supports integratededucation-career exploration and investigation that is more detailed andspecific than the broad picture provided at the primary level. Alllevels utilize user attributes as a dependable delimiter of options, toobtain reliable, individualized solutions.

The quaternary level 220 provides personalized educational scenarios indetail, using extremely detailed information available from the tertiarylevel. For example, courses, course descriptions, course equivalencies,curricula requirements, and formal education standards are used.

FIG. 2B illustrates the primary, secondary, tertiary, and quaternarylevels and sublevels (or modules), according to one embodiment of thepresent invention. Primary level 205 is Institutions, Funding &Employers. The sublevels are, for example: Agencies & Companies 201,Industries 202, Engineering Disciplines 203, Colleges 204, FinancialResources 224, and Geographical Locations 206.

Secondary level 210 is Pursuits. The sublevels are, for example:Internships 207, Job Links 208, Job Descriptions 209, EngineeringCareers 225, Strategies for Employment 211, and Geographic Locations212.

Tertiary level 215 is Programs & Standards. The sublevels are, forexample: College Program Types 213, College Programs 214,Distance/Online Programs 226, Secondary School Standards 216, CollegeEntrance Testing 217, and Geographical Locations 218.

Quaternary level 220 is Curricula & Courses. The sublevels are, forexample: Curricula 219, Course Types 227, Course 221, Course Tutoring222, and Geographic Locations 223.

The base data filtering system is a two-tier filter system: (1)intra-module filtering and (2) inter-module filtering. FIGS. 2C-2Fillustrate logic diagrams indicating how the modules may be undergointra-filtering, according to one embodiment of the present invention.Thus, for example, large datasets from module C3 (Job Descriptions) maybe filtered (i.e., delimited for the user) within that module by optionsin Occupation, Employment Level, Education Level, Salary Range and JobTitle.

Furthermore, FIGS. 2G and 2H illustrates a data navigation logic diagramand chart indicating how the levels may in turn undergo inter-levelfiltering, according to one embodiment of the present invention. Thus,for example, information from module R1 (Agencies & Companies) may befiltered (e.g., delimited with) information from modules R2(Industries), R3 (Engineering Disciples), R5 (Financial Resources),RCPA-GL (Geographical Locations), C1 (Internships), C2 (Job Links), C3(Job Descriptions), C4 (Engineering Careers), P1 (College ProgramTypes), A1 (Curricula), and A2 (Course Types).

FIG. 2I is a flowchart diagram illustrating how levels and sublevels arefiltered, according to one embodiment of the present invention. Theexample of filtering in sublevel R1 (Agencies & Companies) is shown. Instep 250, a list of modules is displayed. In step 251, a user selects amodule. In step 252, it is determined whether the module is “Agencies &Companies”. If no, in step 253, further navigation takes place. If yes,in step 254, a list of company types is displayed. In step 255 the userselects a company type. In step 256, a list of company sizes isdisplayed. In step 257, the user selects a company size. In step 258, alist of options for a company location is displayed. In step 259, theuser selects an option for the company location. In step 260, it isdetermined if the company location option selected is regions. If no, instep 261, it is determined if the company location option selected issubregions. If no, in step 252, it is determined if the company locationoption selected is states. If no, in step 263, the list of all companiesmatching the type and size criteria is displayed. If some companies weremarked during previous navigation, they are marked again.

In step 264, if the company location option is states, the list ofstates is displayed. In step 265, the user selects at least one state.In step 266, a list of companies in the selected state(s) matching thetype and size criteria is displayed. If some companies were markedduring previous navigation, they are marked again.

In step 267, if the company location option is subregions, a list ofsubregions is displayed. In step 268, the user selects at least onesubregion. In step 269, a list of companies in the selected subregion(s)matching the type and size criteria is displayed. If some companies weremarked during previous navigation, they are marked again.

In step 270, if the company location option is regions, a list ofregions is displayed. In step 271, the user selects at least one region.In step 272, a list of companies in the selected region(s) matching thetype and size criteria is displayed. If some companies were markedduring previous navigation, they are marked again.

In step 273, the user can mark (choose) at least one company. In step274, the user can view detailed information about the marked companies.The process then returns to step 250 and repeats.

Application Overview

FIGS. 3A and 3B illustrate application overview 300, according to oneembodiment of the present invention. The application overview 300comprises a presentation layer 305, a business logic layer 310, and adata access layer 315.

Presentation Layer. The presentation layer 305 comprises a list tool 306and a detail window 307.

List Tool. The list tool 306 is used for navigational purposes. The listtool 306 displays a list of items (e.g., representing modules, tables,limiters, ranges, and items from the database). Graphical representationof the items is different for different types of items. The item cancontain, for example, a checkbox, various forms of highlighting, anddifferent appended icons. The list tool 306 uses orbital navigation,which is an unrestricted always all-forward navigation. Back navigation(e.g., undo level) is also supported in one embodiment. The list tool306 allows long lists to be displayed in a way that allows intra-modulefiltering and inter-module filtering options. The list tool 306 alsoallows orbital navigation.

Detail Window. The detail window 307 displays detailed information aboutthe items selected in the list tool 306. The detail window 307 alsoenables comparison of the items. The detail window 307 displays textualinformation together with all relevant multimedia information (e.g.,audio, pictures, video files). The detail window 307 effectively usesthe available display area by dynamically changing the sizes of thedisplayed objects. The detail window 307 displays all relevantinformation in one place. The detail window 307 also performs anintelligent comparison of particular items together with a collateralview. The detail window 307 also dynamically changes the viewing area sothat an item of interest occupies more area than other items.

Business Logic Layer. The business logic layer 310 comprises a history311, a basket 312, a filtering algorithm 313, and a multimediaintegrator 314.

Filtering Algorithm. The filtering algorithm 313 limits the number ofpossibilities according to previously selected data. The filteringalgorithm 313 works with data in the database and with lists of previousselections, and uses the database model to dynamically and effectivelycreate and optimize queries. The filtering algorithm 313 allows queriesto be constructed “on the fly” and uses data models to create queries.

History. The history 311 remembers visited items (e.g., ranges,limiters, modules, module items) and enables easy navigation to thevisited items. The history 311 stores lists of previously displayeditems, and if the user clicks on an item in the list, the history 311enables displaying of that item.

Basket. The basket 312 stores items selected by user into a formattedrepository. The basket 312 stores items checked by a user, keeps a usedlist generation (i.e., items that were checked previously must bechecked when the list is displayed again). The basket 312 also preservesstored items for (re)display, printing or sharing. In addition, storeditems can be sent to another user (e.g., a counselor) for review.

Multimedia Integrator. The multimedia integrator 314 gathers allrelevant data from the disparate databases into one coherent whole,personalized for the user, and advises a user how to continue navigatingthe present invention. The multimedia integrator 314 uses multimediafiles together with database information and filtering processes todisplay all information. In addition, all information is displayedintelligently at one place. It also uses the history of visited modulesand items to recommend for the next navigation. The multimediaintegrator also makes intelligent recommendations for further pathapplication.

Data Access Layer (Data Storage). The data access layer 315 comprisesdatabase logic 316, database 317, and multimedia files 318.

Database Logic. The database logic 316 is a communication level betweenthe application logic and the data. It creates responses to dataqueries, provides simple manipulations with queries using the databasemodel (no history or other session data is used during thesemanipulations), and sends queries to the database and provides simplemanipulations with the results.

Database. The database 317 stores all textual data and all lists used.It also stores all relations between the data. The database can alsoretrieve requested data quickly and efficiently.

Multimedia files. The multimedia files 318 are displayed in theapplication.

Filtering Method Overview

FIG. 4 illustrates a method of filtering data 400, according to oneembodiment of the present invention. In step 405, the user enters afiltering request (e.g., a request to navigate the data- translated as arequest to use the data as a delimiter, or as a constraint object). Instep 410, a start table and an end table in the database are determined.The start table is determined by the module the user was in when theuser started navigating, and the end table is determined by therequested module (where the user wants to end navigating). In step 415,it is determined if the start table and the end table are closelyrelated. The start table and end table are closely related if thereexists a direct relationship in the data model. If the start table andthe end table are not closely related, in step 420, it is determined ifthe start and end tables are loosely related. The start table and endtable are loosely related if there is no direct relationship in the datamodel, but there is an indirect relationship (i.e., there is a route inthe data model from one table to another table via an interim table ortables). In one embodiment, the maximum number of interim tables is setto two, or another function exists to related items from one table toanother (e.g., the function is strictly case oriented, or specific forcertain tables and built using external knowledge). If the start tableand the end table are not loosely related, in step 421, the filteringalgorithm cannot be used because there is no utilizable information. Inthis case, all items from the end table are displayed and the processends. If the start table and the end tables are loosely related, in step422, a factor is set to “low value”. The process then moves to step 430.

If the start and end tables are closely related, in step 425, the factoris set to “high value”. The process then moves to step 430, where it isdetermined if the end table contains any unprocessed items. Thisalgorithm processes all items, one after another, in a sequential manner(i.e., one item at a time, one after another). If the end table does notcontain any unprocessed items, data from the end table that meet thefiltering criteria are displayed in step 435.

If the end table does contain any unprocessed items, in step 440, thenext item is taken from the end table. In step 445, the relations dataand items from the start table are used to adjust the degree of how theitem meets the filtering criteria, multiplied by a factor. In thisprocess, the overall degree of item compatibility is calculated. Thedegree is defined as a sum of particular compatibilities with particulartables (e.g., filtering criteria). Each particular compatibility iscomputer first. Then the computer compatibility is multiplied by afactor, so the proximity of relation (e.g., its importance) is takeninto account. In step 455, it is determined if there is any preclusiveconditions that are met. For each table, a defined set of preclusiveconditions is set. If any of these conditions is met, the filteringalgorithm knows that the considered item is not acceptable as acompatible result. The preclusive conditions are defined strictly forspecific tables and typically uses specific data in items and specificexternal information (e.g., from a suer's profile). If there are anypreclusive conditions that are met, in step 460, the item is notconsidered to meet the filtering criteria. The process then returns tostep 430.

If there are not any preclusive conditions that are met, in step 465, itis determined if the degree is higher than the specified threshold. Aspecific threshold value is set for a particular solution. The thresholdvalue is determined experimentally, in some cases. If the value is toohigh, few items are considered to be compatible. If the value is toolow, too many items are considered to be compatible. If yes, in step470, item is considered to meet the filtering criteria, and the processreturns to step 430. If no, in step 475, it is determined if there is atable deeper in the history that has not been processed. The historycontains a list of tables that were used during navigation in the past.This program determines a level of compatibility for each item in theend table for each table in the history. Tables in the history arestored in an array and are taken one after another. If there is not adeeper table in the history, in step 460, the item is not considered tomeet the filtering criteria, and the process returns to step 430. Ifthere is a deeper table in the history, in step 480, this table isdesignated as the new start table, the factor is decreased in step 485,and the process returns to step 415.

Additional Features

Adaptive Graphical User Interface (GUI). Rather than using a“one-size-fits-all” GUI, the adaptive GUI personalizes the GUI's “lookand feel”, using the user's characteristics (e.g., age, gender, andmaturity) to maximize the user's experience. For example, a GUI foradolescent females maybe chosen that displays videos of women in theworkplace.

The adaptive GUI adapts to the user's profile in at least two ways: itoptimizes the layout of the GUI for optional user experience inperforming tasks, and it optimizes the function of the GUI.

To optimize the layout of the GUI elements that are identified asprofile-relevant by the personal agent factor (PAF), a layoutappropriateness (LA) method is used. The LA method computes the layoutappropriateness of an interface by assigning frequencies and costs totask descriptions (i.e., sequences of user transitions between GUIelements) involved in performing specific tasks with the interface. Thecosts are derived from the distance a user must travel between GUIelements and also to an index of difficulty (e.g., Fitts Index ofDifficulty).

The LA method enables the in-situ generation of a user-tailored,user-optimal layout, until the system again recognizes an off-toleranceuser profile change. User profile information (e.g., for user behaviorduring application use, as captured by the assessment manager; fromassessment scores; or from direct user input), if within the norm, willadd no changes to the functionality of the base GUI components, and thusthe GUI display. However, new user profile elements (e.g., usebehavior), once outside the set norm references, will effect functionalchanges to the base GUI components, and potential layout changes to GUTdisplay.

In optimizing the adaptive GUI function, to better provide the user withtailored resources, the present invention uses a personal agentframework (PAF). The PAF coordinates numerous user profile files. Thus,the user's application user behavior is evaluated continuously duringinteraction with the application, and the user's profile could changeaccordingly.

Optimizing the function takes place by linking GUI objects to userprofile elements using the PAF. The PAF links the GUI elements with thedynamic repository of user profiles. The PAF also stores objects in amultimedia solution database. After the multimedia solution database hasbeen populated via pilot test and continuous user data capture, PAF usesits case-based learning module to improve and speed up the rate at whichit generates user profile-GUI element combinations by matching theprofile of the new user with those for which combinations exist in thesolution repository. A PAF profile manager acquires and stores userprofiles (e.g., user-input personal data, interest topics, assessmentresults, user habits) and manages user interest hierarchy.

The adaptive GUI can be used for, for example, adaptive learningproducts, involving intelligent tutoring, self-paced, self-directededucation, computer based educational and career assessment tools, andadult learning tools and products.

FIG. 5 illustrates a method of using the adaptive graphical userinterface 500, according to one embodiment of the present invention. Instep 505, output results from an advanced self-concept assessmentinstrument (or multimedia questionnaire), as well as results from otherappropriate assessment instruments are fed into an assessment manager instep 510. In step 510, the assessment manager serves to organize theassessment results and user behavior parameters into profile elements.In step 515, the profile elements for the user are then categorized andclassified into norm-referenced user profiles, and stored into a “userprofiles” database. In step 520, a comparison of a current user profileis made with the norm. In step 525, if the profile is within the norm,the base GUI components are activated, and if already activatedpreviously, then the GUI display remains as is in step 550 and there isno change to the GUI. If the user profile is outside of the norm, step530 activates the personal agent factor (PAF). The PAF innovation servestwo key purposes: first, it will use a knowledge base of user profilesand AI techniques to mine, organize and report useful, individualizedinformation or solutions back to the user. Secondly, the PAF will assignGUI component parameter values to user profile elements, thus providingthe basis for changes to base GUI components. In step 535, those changesare implemented in the base GUI components. Step 540 determines whetherthe changes to the function of the base GUI components have resulted inchanges to the frequencies of key tasks of the application. If so, thena new GUI layout is generated in step 545 and displayed in step 550, andif not, the GUI remains the same in step 550, with no changes to thecurrent GUI setting.

The adaptive GUT continuously monitors and captures user behavior duringapplication use, and continuously compares that application use behaviorto stored values of the norm. In step 555, if user behavior is withinthe norm, there is no change to the nominal base GUI components of step525, and the display remains the same in step 550. If user behavior isoutside of norm however, that information is passed to the assessmentmanager step in 510 for processing and subsequent generation of new userprofile elements.

Assessment Combinator. The objectives of the assessment combinator aretwo-fold: (1) Create relevant combinations of assessment items acrossassessment batteries (i.e., new assessment scales obtained by combiningquestion items from different assessment instruments) and (2) Assigninferences on combination results to choice options. The assessmentcombinator will thus be an efficient match-enabler for integratededucation-career options by providing a searchable “library” of newcombinator result-to-choice option assignments. The assessmentcombinator will resolve the issue of the systematic assignment of newcross-instrument measures to attributes, and the systematic assignmentof such attributes to choice options. If successful, the value-addedhere will be the generation of a large number of additionalcross-instrument sub-scales, with a minimal number of their associatedmeasures able to point users to choice options that existing instrumentsare currently inherently unable to do. For example, in the case of twoconventional instruments (or questionnaires) A and B, each with threeassessment components (each requiring a “Yes”/“No” response), themaximum number of intra-instrument sub-scales from each instrument wouldbe {3C1+3C2+3C3}, or seven, for a total of fourteen (14) sub-scales fromboth instruments. However, the maximum additional number of componentcombinations from both instruments, to create new possiblecross-instrument sub-scales would then be the square of {3C1+3C2+3C3},or forty-nine (49) sub-scales, for a sub-scale total of 63.

The successful use of measures from some of the new sub-scales as newpredictive decision pointers will be a significant extension of thestate-of-the-art. Such a development will open up new possibilities fordecision support, enhance the efficiency and utility of existingdecision tools, and maximize the usefulness to the user of user-suppliedassessment information.

FIG. 6 illustrates a method of using an assessment combinator 600,according to one embodiment of the present invention. In step 605, atleast one question from instrument #A is entered. In step 610, at leastone question from instrument #B is entered. Instrument #A and instrument#B are, for example, questionnaires related to preferences, skills,abilities, temperment, self-concept, decision-making ability, etc. Thequestionnaires can be on paper or computerized. An example of a questionfrom instrument #A is “Do you like working in a team?”. An example of aquestion from instrument #B is “Do you like math?”. In step 615, atleast one answer from instrument #A and at least one answer frominstrument #B are combined. In step 620, it is determined if the #A and#B combination translates into at least one user attribute by searchinga database of user attributes to see if there is a match. In thisexample, if a person likes working on a team and likes math, a userattribute can be, for example, that the person is a technical teamplayer. If not, in step 625, the #A and #B combination is discarded. Ifyes, the #A and #B combination is assigned to the at least one userattribute in the database of user attributes. Thus, in this example, thecombination is assigned a user attribute of a technical team player. Instep 635, it is determined if the user attribute combination translatesto a decision option. Thus, in the example, the choice would be anengineer. If not, in step 625, the user attribute combination isdiscarded. If yes, in step 650, the user attribute combination isassigned to the at least one decision option.

Self-Concept Assessor. Inaccurately measuring a person's self-concept(e.g., interest and skills) provides inaccurate education and careerchoice options. Conventional self reports that assess self-concepts(e.g., rating scale) often result in a masked measure for a self-concept(e.g., a person will answer questions according to social expectationsinstead of real feelings). The present invention provides a self-conceptassessor that captures direct user feedback that is not masked. Thepresent invention does not require substantial verbal skills, inherentlyreminds a user of his/her own perceptions, and requires a low “socialdesirability” response. In addition, the present invention separates twoembedded utilities: (1) the expression of a range of self-efficacybeliefs in a multi-media presentation for the user to react to invarious levels of distinction, and (2) subtle references to accuracycriteria in the same multimedia presentation.

The present invention includes at least one of the following features:

-   -   Levels of occupational and academic interest and skills are        assessed.    -   Techniques that transfer self-efficacy beliefs into a        multi-media presentation (e.g., video, pictures, animations) for        a user to react and respond to (e.g., concur with, disagree        with, or neutral) are used, where the multi-media presentation        also embeds a criterion of accuracy.    -   Responses to items are in a Likert-type response format (e.g.,        concur with, disagree with, neutral) with various levels of        distinction (e.g., strong agreement, complete agreement).    -   Interests, skills and occupational scales scores will be        reported and integrated into the user profile database.    -   A criterion-referenced approach, in which the user's        self-concept beliefs are assessed repeatedly in reference to an        external criterion of accuracy, rather than to a norm, is used.    -   The self-concept assessor takes advantage of education and        career setting video images incorporated into an “exploration        function” of the architecture. It uses a criterion-referenced        approach, where a user's self-concept beliefs are assessed        repeatedly in reference to an external criterion of accuracy,        built around video clips, rather than an approach that compares        a user's response against a set of norms. The self-concept        assessor captures self-efficacy thoughts, in as filter-free a        manner as practicable.

FIG. 7 illustrates a method of using a self-concept assessor 700,according to one embodiment of the present invention. In step 705, aself-concept assessment instrument is created. For example, a video iscreated showing day-to-day activities of an attorney. The user issuper-imposed in the video. In step 710, a response combination toattribute ranks is classified. Thus, for example, the user can answerquestions about the video, and the responses are classified. In step715, attributes are assigned to a decision option. In the example, ifthe user indicated that he liked the attorney occupation, the careeroption of an attorney is designated. The attributes and career optionscan be ranked.

Education Plan Designer. The explosion of education options and pathsnecessitates a mechanism that enables students' exploration of severalexplore options. The education plan designer provides a convenient toolfor user-friendly creation, manipulation, display, and review ofeducational curricula, using at least one of the following features:

-   -   Enables a user to design a new course plan, or modify an        existing plan towards completing a degree at a specific        institution, that will lead to a desired career path.    -   Enables a user to investigate and if desired, articulate and        thus substitute courses with other compatible, institutionally        acceptable courses from a variety of sources (e.g., neighboring        institutions, e-learning sites).    -   Enables a user to investigate and if desired, articulate current        curricula with other curricula, with a view to exploring the        various implications (career and otherwise) of a change of        institution and/or major area of study.    -   Enables a user (enrolled or un-enrolled) to perform their own        investigations related to transferring, with due regard to        required and elective course options, and the career and        employment implications of course choices.    -   Enables a user to perform audits on their current curriculum        towards determining graduation prospects and timing.

The educational plan designer implements the congruence of the educationuniverse with the other two universes of careers and personalattributes. This changes the way students navigate the educationalprocess, by potentially putting in the hands of all students, whethercurrently enrolled or not, the resources and tools to review, plan anddesign their own educational plan.

The education plan designer imports the entire curricula, programelements, accompanying protocols and Boolean requirements from a set ofinstitutions relevant to a specific career and educational path into aseries of updatable databases. The education plan designer thensimulates the process of student advising, transfer student auditing,and curricula design, but does it with an entire advisory environmentfrom the relevant institutions, providing design tools to review,initiate, re-build, and investigate options with significant savings intime. The user will be able to: design a new course plan, or modify anexisting one for academic work at a specific institution, that will leadto a desired career path; investigate and if desired, articulate courseson the primary plan with institutionally acceptable substitute coursesfrom neighboring institutions; investigate and if desired, articulatecurrent curricula with other curricula, to explore implications (e.g.,career, graduation) of a change of institution/major area of study; andperform regular/transfer student advising, with due regard torequired/elective course options, and their employment and/or internshipimplications.

Example uses of the education plan designer include: articulatingtransfer students quickly and efficiently for educational institutions;and embedding the education plan designer in existing products forcollege-bound students for software publishers.

FIG. 8 illustrates a logic diagram of using the education plan designer800, according to one embodiment of the present invention. Databasesinclude course types 805, courses 810, curricula 815, standards 820, andinformation on course tutoring 845. Programs include course articulationsystem 850, advisory/personalized curricula 855, curricular articulationsystem 860, custom course plan designer 870, pre-frosh advising 875,regular student advising system 880, regular student audit system 885,transfer student advising system 890, transfer student audit 895, andcurricular performance 896. FIG. 8 illustrates how the databases andprograms are logically connects. T represents tertiary, Q representsquaternary, S represents services, and I represents information.

Adaptive Backsteppable Filter. Finding a dynamic intersection, in termsof options, among career, education and user attribute databasesrequires a robust data integrator that efficiently organizes the vastamounts of multimedia data in these databases for logical filtering. Theadaptive backsteppable filter performs this task. The adaptivebacksteppable filter is a three-stage series of data integrator-filters.Stage I dynamically aggregates and stores objects created fromcombinations of related education database data and career databasedata. Stage I then forwards a copy of these new objects to Stage II toenable a rejoining of the objects with compatible user profile elementsto obtain new “education-user profile objects” and “career-user profileobjects”. Stage III dynamically creates new objects to obtain“education-career-user profile objects”. These integrated objects arethen instantly available to the front-end as display-ready information,improving query efficiency and accuracy. The user may also re-engineer asolution by back-stepping to recall how options and paths were derived,providing a useful function to reviewers of user decision processes(e.g., counselors).

FIG. 9 illustrates a method of using the present invention, highlightingthe adaptive backsteppable filter 900, according to one embodiment ofthe present invention. The user takes an assessment instrument todiscover an initial user profile using the adaptive GUI 905. The userprofile undertakes several assessment instruments 910 (e.g.,self-concept assessor, assessment manager) and then proceeds to adatabase of user attributes 910. The user attribute information isprovided to the adaptive backsteppable filters 915, 916, and 917, andthe personal agent framework 920. The list tool mechanism will then beused to limit the information that is shown. At this point, the GUI 905can be adapted, if necessary. Now that the user has an adapted GUI 905,information from the databases 931, 932, 933, 934, 935, and 936 ispulled using the database navigators 950 to navigate all the databases.This filtered information is displayed on the GUI 905. It is also storedin the in the repository 955 (i.e., history/basket).

Integrated Assessor. The integrated assessor is the end-use computerimplementation, in software, of the assessment combinator functionality.It is the process for integrating assessment combination assignmentsinto a computer application for direct use by the user. The utility andbenefit of the assessment combinator will be completely lost to theuser, without the ability to incorporate the new cross-instrumentassessment scales and ensuing measures into an application'sdecision-making mechanism. This involves the creation of a function thatstores cross-instrument scale combinations (new sub-scales), andattribute assignments of their potential measures, in a data repository,much like a searchable library, such that choice options are recalled,whenever combinations are matched by the user.

The integrated assessor is illustrated as step 650 of FIG. 6, accordingto one embodiment of the present invention. The integrated assessorcombines steps 630 and 640 of FIG. 6.

Assessment Manager. The assessment manager serves to organize theassessment results and user behavior parameters into profile elements.It engages in processing and generation of user profile elements for useas delimiters that filter user choice options. Thus, as the assessmentmanager captures user behavior parameters during application use, orprocesses assessment results into profiles for immediate use asdelimiter filters, it effectively acts as a “just-in-time” administratorand implementer of assessment results, due to a capability as a“just-in-time” generator of user profile elements.

FIG. 10 illustrates a method of using the assessment manager 1000,according to one embodiment of the present invention. In step 1005, ahistory of user navigation patterns is input. In step 1010, userassessment results are input. In step 1015, the assessment managergenerates and stores a user profile. In step 1020, delimiting(filtering) functions are performed.

Multimedia Information Integrator and Navigator. The multimediainformation integrator and navigator represents a database managementfunction to effectively and efficiently integrate information from thethree universes of potential careers, potential educational paths andstudent attributes. It enables the entire application to display theattributes of integrated functionality. The multimedia informationintegrator and navigator also allows the application to displayrecommended paths to the user in an integrated manner, to allow them tonavigate through the database in a way that will most likely help theuser more quickly reach their goals.

Solution Analyzer. The solution analyzer provides tools and algorithmsfor extracting and analyzing education-career solution information andsharing it with others. The solution analyzer extracts education-careersolution information from solution repositories throughout theapplication, and then creates a formatted analysis of the extractedsolution, on a multimedia template that can be easily shared with otherstakeholders. The solution analyzer provides the user with summaryinformation about an investigated solution option, including rationalebehind solution options. A detailed solution option analysis allows theuser to identify flaws, in the input information and assumptions thatgenerated the solution path. The solution analyzer allows the user tomake critical changes that may lead to a new, more realistic, morecompatible and more desirable solution option.

Screen Shots

Curricular Designer Screen Shots. FIGS. 11A-11S illustrate screen shotsthat present the entire curriculum sequence of courses, from semester tosemester, where the user is able to perform the following functions:Review the details about a course, from descriptions to reviews ofsyllabi, course objectives, course expected outcomes, course resources,grading policies, course schedule, archive of student's reviews; Adding,dropping and choosing options about what course to take, from a databaseof courses that include courses from all institutions in the country;finding options about repeating courses; Inputting grades; TrackingGrade Point Averages (GPA's); Modeling future GPA's; Tracking financialcosts, financial statistics, academic statistics; Launching aself-guided intelligent tutor to assist with course tutoring; Trackingcredits and time needed to graduate, and its projected costs;Investigating the implications of taking a course on future employmentor internship opportunities; Investigating the implications of taking aparticular course and having a particular GPA on financial aid;Articulating courses with similar courses from appropriate institutionsin the country that have closely similar (articulated) courses;Reviewing the possibility for articulating existing courses withmatching, accepted courses; Generating an informal transcript; Reviewsof merit-based and non-merit-based financial resources; and,Articulating whole curricula with other curricula from appropriateinstitutions in the country, in order to identify similarities intransfer courses (for transfer students).

Explore/Job Market Screen Shots. FIGS. 12A-12U illustrate explore/jobmarket screen shots. FIG. 12A illustrates the main categories and levelsof the application. FIGS. 12B to 12G take a user from the first level toadditional sub-levels, to obtain module and sub-module lists. FIGS. 12Hto 12I illustrates succeeding navigation proceeding in an “alwaysforward” mode (referred to as orbital navigation). FIGS. 15J to 15Tillustrates list of modules within which user has to choose choiceoptions, and the program then presents to the user the possibilities forthe remaining categories. For each choice, user is able to review vastmultimedia content on the choice item before making a decision tochoose. FIG. 15U is an assessment user interface.

The present invention is described in terms of the above embodiments forconvenience only, and this is not intended to limit the application ofthe present invention. It will be apparent to one skilled in therelevant arts how to implement the present invention in alternativeembodiments. In addition, the Figures and screen shots described above,which highlight the functionality and advantages of the presentinvention, are presented for example purposes only. The architecture ofthe present invention is sufficiently flexible and configurable, suchthat it may be utilized in ways other than that shown in the Figures andscreen shots. Further, the purpose of the Abstract is to enable the U.S.patent and Trademark Office and the public generally, and especially thescientists, engineers and practitioners in the art who are not familiarwith patent or legal terms or phraseology, to determine quickly from acursory inspection the nature and essence of the technical disclosure ofthe application. The Abstract is not intended to be limiting as to thescope of the present invention in any way.

1. A method for creating an accurate user profile, comprising: capturingat least one user reaction to at least one self-concept instrument;classifying the at least one user reaction as at least one attribute;and assigning a decision option to the at least one attribute.
 2. Asystem for creating an accurate user profile, comprising: a userinterface for capturing at least one user reaction to at least oneself-concept instrument; a program for classifying the at least one userreaction as at least one attribute; and a program for assigning adecision option to the at least one attribute.